Hyperspectral imaging system for monitoring agricultural products during processing and manufacturing

ABSTRACT

Provided is a method for monitoring a manufacturing process of an agricultural product. The method utilizes hyperspectral imaging and comprises scanning at least one region along a sample of agricultural product using at least one light source of a single or different wavelengths; generating hyperspectral images from the at least one region; determining a spectral fingerprint for the sample of agricultural product from the hyperspectral images; and comparing the spectral fingerprint so obtained to a spectral fingerprint database containing a plurality of fingerprints obtained at various points of the manufacturing process, using a computer processor, to determine which point in the manufacturing process the sample has progressed to.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Ser. No. 14/443,986, filedMay 19, 2015, which is a National Stage Entry of PCT/US2013/070809,filed Nov. 19, 2013, which is a non-provisional application of U.S. Ser.No. 61/728,123, filed Nov. 19, 2012, which is incorporated by referenceherein in its entirety.

FIELD

Disclosed herein is a method and system for monitoring the processing ofagricultural products, such as tobacco, using hyperspectral imaging andanalysis. Embodiments disclosed herein can be practiced with otheragricultural products, including but not limited to, tea, grapes,coffee, vegetables, fruit, nuts, breads, cereals, meat, fish and otherplant or animal parts.

ENVIRONMENT

Certain agricultural products, such as cultivated tobacco plants tea,spinach, etc., are harvested for their leaves, which may then be driedand cured as in the case of tobacco or tea. In the manufacture ofcigarettes and other tobacco products, different types of tobaccos arefrequently processed, with three main types of tobacco used in U.S.blends. These tobacco types are Virginia or flue-cured, Burley andOriental. In the following, the method and the system disclosed hereinare illustrated for application in the tobacco industry, though the samemethod and system could be used for other agricultural products usingleaves like tea and spinach, or in other packaged agricultural productsusing fruits, grapes, tomatoes, and other vegetables.

Tobacco, in many cases, contain tobacco materials that have processedforms, such as processed tobacco stems (e.g., cut-rolled stems,cut-rolled-expanded stems or cut-puffed stems), volume expanded tobacco(e.g., puffed tobacco, such as dry ice expanded tobacco (DIET),preferably in cut filler form). Tobacco materials also can have the formof reconstituted tobaccos (e.g., reconstituted tobaccos manufacturedusing paper-making type or cast sheet type processes). Tobaccoreconstitution processes traditionally convert portions of tobacco thatnormally might otherwise be treated as waste, into commercially usefulforms. For example, tobacco stems, recyclable pieces of tobacco andtobacco dust can be used to manufacture processed reconstituted tobaccosof fairly uniform consistency. See, for example, Tobacco Encyclopedia,Voges (Ed.) p. 44-45 (1984), Browne, The Design of Cigarettes, 3.sup.rdEd., p. 43 (1990) and Tobacco Production, Chemistry and Technology,Davis et al. (Eds.) p. 346 (1999).

Various representative tobacco types, processed types of tobaccos, typesof tobacco blends, cigarette components and ingredients, and tobacco rodconfigurations, also are set forth in U.S. Pat. No. 4,836,224 to Lawsonet al.; U.S. Pat. No. 4,924,883 to Perfetti et al.; U.S. Pat. No.4,924,888 to Perfetti et al.; U.S. Pat. No. 5,056,537 to Brown et al.;U.S. Pat. No. 5,159,942 to Brinkley et al.; U.S. Pat. No. 5,220,930 toGentry; U.S. Pat. No. 5,360,023 to Blakley et al.; U.S. Pat. No.6,730,832 to Dominguez et al.; and U.S. Pat. No. 6,701,936 to Shafer etal.; U.S. Patent Application Publication Nos. 2003/0075193 to Li et al.;2003/0131859 to Li et al.; 2004/0084056 to Lawson et al.; 2004/0255965to Perfetti et al.; 2005/0066984 to Crooks et al.; and 2005/0066986 toNestor et al.; PCT WO 02/37990 to Bereman; and Bombick et al., Fund.Appl. Toxicol., 39, p. 11-17 (1997); which are incorporated herein byreference.

Tobacco also may be enjoyed in a so-called “smokeless” form.Particularly popular smokeless tobacco products are employed byinserting some form of processed tobacco or tobacco-containingformulation into the mouth of the user. Various types of smokelesstobacco products are set forth in U.S. Pat. No. 4,528,993 to Sensabaugh,Jr. et al.; U.S. Pat. No. 4,624,269 to Story et al.; U.S. Pat. No.4,987,907 to Townsend; U.S. Pat. No. 5,092,352 to Sprinkle, Ill et al.;and U.S. Pat. No. 5,387,416 to White et al.; U.S. Pat. Appl. Pub. Nos.2005/0244521 to Strickland et al.; and 2009/0293889 to Kumar et al.; PCTPat. App. Publ. WO04/095959 to Arnarp et al.; PCT Pat. App. Publ.WO05/063060 to Atchley et al.; PCT Pat. App. Publ. WO05/016036 toBjorkholm; and PCT Pat. App. Publ. WO05/041699 to Quinter et al., eachof which is incorporated herein by reference. See, for example, thetypes of smokeless tobacco formulations, ingredients, and processingmethodologies set forth in U.S. Pat. No. 6,953,040 to Atchley et al. andU.S. Pat. No. 7,032,601 to Atchley et al., each of which is incorporatedherein by reference.

Dark air-cured tobacco is a type of tobacco used mainly for chewingtobacco, snuff, cigars, and pipe blends. Most of the world production ofsuch tobacco is confined to the tropics; however, sources of darkair-cured tobacco are also found in Kentucky, Tennessee, and Virginia.Dark air-cured tobacco plants are characterized by leaves with arelatively heavy body and such tobacco plants are typically highlyfertilized and topped low to around 10-12 leaves. See TobaccoProduction, Chemistry and Technology, Davis et al. (Eds.) pp. 440-451(1999).

The manner in which various tobacco varieties are grown, harvested andprocessed is well known. See, Garner, USDA Bulletin No. 143, 7-54(1909); Darkis et al, Ind. Eng. Chem., 28, 1214-1223 (1936); Bacon etal., USDA Tech. Bulletin No. 1032 (1951); Darkis et al., Ind. Eng.Chem., 44, 284-291 (1952); Bacon et al., Ind. Eng. Chem., 44, 292-309(1952); Curing Flue-Cured Tobacco in Canada, Publication 1312/E (1987);and Suggs et al., Tob. Sci., 33, 86-90 (1989). See, also, Hawks, Jr.,Principles of Flue-Cured Tobacco Production, 2.sup.Ed. (1978);Flue-Cured Tobacco Information 1993, N. C. Coop. Ext. Serv.; and Peeleet al., Rec. Adv. Tob. Sci., 21, 81-123 (1995). Those references areincorporated herein by reference. In general, harvesting includesdisrupting the senescence process by removing tobacco leaves from theplant at a desirable point in the plant life cycle.

It has been common practice to flue-cure certain tobaccos, such asVirginia tobaccos, in barns using a so-called flue-curing process.Cooper et al., VPI Bull., 37(6), 3-28 (1939); Brown et al., Agric. Eng.,29(3), 109-111 (1948); Johnson et al., Job. Sci., 4, 49-55 (1960);Johnson, Rec. Adv. Tob. Sci., Inag. Vol., 63-78 (1974); Peele et al.,Rec. Adv. Job. Sci., 21, 81-123 (1995). Tobacco to be cured may be grownunder well-known and accepted agronomic conditions, and harvested usingknown techniques. Such tobacco typically is referred to as greentobacco. Most preferably, the harvested tobacco is adequately ripe ormature. Peele et al., Rec. Adv. Tob. Sci., 21, 81-123 (1995). Ripe ormature tobaccos typically require shorter cure times than do unripe orimmature tobaccos.

Under typical conditions green tobacco is placed in an enclosure adaptedfor curing tobacco, commonly referred to in the art as a curing barn.The tobacco will be subjected to curing conditions, typically involvingthe application of heat. The green tobacco can be placed in the barn ina variety of ways, and typically is carried out as a manner of personalpreference. As such, there is wide discretion in the particulardetermination of the amount of tobacco placed within the barn, thepacking density of that tobacco within a box, the spacing of the tobaccowithin the barn, and the location of various tobacco samples within thebarn. See, for example, U.S. Pat. App. Pub. 2001/0386 to Peele andTobacco Production, Chemistry and Technology, Davis et al. (Eds.) p.131-133 (1999). Fire-curing, air-curing, sun-curing, and other curingprocesses are also known in the art.

The conditions of temperature to which the tobacco is exposed duringcuring can vary. The time frame over which curing of the tobacco occursalso can vary. For the flue-curing of Virginia tobaccos, the temperatureto which the tobacco is exposed typically is in the range of about 35°C. to about 75° C.; and the time over which the tobacco is exposed tothose elevated temperatures usually is at least about 120 hours, butusually is less than about 200 hours. Curing temperatures reportedherein generally are representative of the average air temperaturewithin the curing barn during curing process steps. Average airtemperatures can be taken at one or more points or locations within thecuring barn that give an accurate indication of the temperature that thetobacco experiences during curing steps. Typically, Virginia tobaccofirst is subjected to a yellowing treatment step whereby the tobacco isheated at about 35° C. to about 40° C. for about 24 to about 72 hours,preferably about 36 to about 60 hours; then is subjected to a leafdrying treatment step whereby the tobacco is heated at about 40° C. toabout 57° C. for about 48 hours; and then is subjected to a midrib(i.e., stem) drying treatment step whereby the tobacco is heated atabout 57° C. to about 75° C. for about 48 hours. Exposing Virginiatobacco to temperatures above about 70° C. to about 75° C. during curingis not desirable, as exposure of the tobacco to exceedingly hightemperatures, even for short periods of time, can have the effect ofdecreasing the quality of the cured tobacco. Typically, some ambient airpreferably is introduced into the barn during the yellowing stage,significantly more ambient air preferably is introduced into the barnduring the leaf drying stage, and heated air preferably is recirculatedwithin the barn during midrib drying stage. The relative humidity withinthe barn during curing varies, and is observed to change during curing.Typically, a relative humidity of about 85 percent is maintained withinthe curing barn during the yellowing stage, but then is observed todecrease steadily during leaf drying and midrib drying stages. Ofcourse, fire curing and air curing each provide different conditions oftemperature, humidity, and times for various curing steps.

After the tobacco is exposed to curing conditions, the use of heating isstopped. Typically, the fresh air dampers of the barn are opened inorder to allow contact of ambient air with that tobacco. As such,moisture within the ambient air is allowed to moisten the tobacco; andthe very dry freshly cured tobacco is rendered less brittle. The cooledtobacco then is taken down, and the tobacco is removed from the curingbarn. Commonly, fire-cured tobaccos for oral-use tobacco are stored andaged for at least three years after curing is complete, during whichtime anaerobic fermentation occurs. After this, period of anaerobicfermentation storage, the aged tobacco undergoes 5 to 8 weeks of aerobicfermentation in preparation for use in modern moist snuff products,which generally reduces the presence of bitterness-causing compounds inthe tobacco. The long time taken for this traditional curing and agingprocess incurs expenses and delays in production of oral-use/smokelesstobacco.

It has been known practice to cure certain types of tobaccos,particularly specialty tobaccos, using a so-called fire-curing process.Legg et al., TCRC (1986) . It also has been common practice to flue-curecertain tobaccos, such as Virginia tobaccos, in barns using a so-calledflue-curing process, one general description of which is included above.See also Cooper et al., VPI Bull., 37(6), 3-28 (1939); Brown et al.,Agric. Eng., 29(3), 109-111 (1948); Johnson et al., Tob. Sci., 4, 49-55(1960); Peele et al., Rec. Adv. Tob. Sci., 21, 81-123 (1995). Tobaccoleaf is harvested, placed in barns, and subjected to the application ofheat. In recent years, it has been common practice, particularly inNorth America, to cure tobacco using a so-called direct-fire curingtechnique. Typical direct-fire heating units are powered by propane, andduring use, those heating units produce exhaust gases that come intocontact with the tobacco being cured.

One type of smokeless tobacco product is referred to as “snuff.”Representative types of moist snuff products, including those typescommonly referred to as “snus,” have been manufactured in Europe,particularly in Sweden, by or through companies such as Swedish Match AB, Fiedler & Lundgren A B, Gustavus A B, Skandinavisk Tobakskompagni NS, and Rocker Production A B. Exemplary smokeless tobacco products thathave been marketed include those referred to as CAMEL Snus, CAMEL Orbs,CAMEL Strips and CAMEL Sticks by R. J. Reynolds Tobacco Company; GRIZZLYmoist tobacco, KODIAK moist tobacco, LEVI GARRETT loose tobacco andTAYLOR' PRIDE loose tobacco by American Snuff Company, LLC; KAYAK moistsnuff and CHATTANOOGA CHEW chewing tobacco by Swisher International,Inc.; REDMAN chewing tobacco by Pinkerton Tobacco Co. LP; COPENHAGENmoist tobacco, COPENHAGEN Pouches, SKOAL Bandits, SKOAL Pouches, REDSEAL long cut and REVEL Mint Tobacco Packs by U.S. Smokeless TobaccoCompany; and MARLBORO Snus and Taboka by Philip Morris USA.Representative smokeless tobacco products also have been marketed underthe tradenames Oliver Twist by House of Oliver Twist A/S. See also, forexample, Bryzgalov et al., 1N1800 Life Cycle Assessment, ComparativeLife Cycle Assessment of General Loose and Portion Snus (2005). Inaddition, certain quality standards associated with snus manufacturehave been assembled as a so-called GothiaTek standard.

As in the case of any agricultural product, tobacco may be characterizedby a wide variety of physical, chemical, and/or biological properties,characteristics, features, and behavior, which are associated withvarious aspects relating to agriculture, agronomy, horticulture, botany,environment, geography, climate, and ecology of the tobacco crop andplants thereof from which tobacco leaves are derived, as well as themanner in which the tobacco has been processed, aged or fermented toproduce the sensorial characteristics sought to be achieved by suchfurther processing. Moreover, as those skilled in the art will plainlyrecognize, these characteristics can vary in time throughout furtherprocessing, aging or fermentation.

In summary, the types of processes and times involved in processingtobacco for curing vary, and include air curing, flue curing, firecuring, and other curing processes. It would be desirable to provide asystem and method for monitoring and optimizing these manufacturingprocesses which alter the character and nature of tobacco useful in themanufacture of tobacco products. Likewise, it would be desirable toprovide a system and method for monitoring and optimizing themanufacturing processes of other agricultural products, including, butnot limited to, tea, spinach, fruits, vegetables, etc.

In the general technique of hyperspectral imaging, one or more objectsin a scene or sample are affected in a way, such as excitation byincident electromagnetic radiation supplied by an external source ofelectromagnetic radiation upon the objects, which causes each object toreflect, scatter and/or emit electromagnetic radiation featuring aspectrum.

Hyperspectral imaging and analysis is a combined spectroscopy andimaging type of analytical method involving the sciences andtechnologies of spectroscopy and imaging. By definition, hyperspectralimaging and analysis is based on a combination of spectroscopy andimaging theories, which are exploitable for analyzing samples ofphysical, chemical, and/or biological matter in a highly unique,specialized, and sophisticated, manner.

Hyperspectral images generated by and collected from a sample of mattermay be processed and analyzed by using automatic pattern recognitionand/or classification type data and information processing and analysis,for identifying, characterizing, and/or classifying, the physical,chemical, and/or biological properties of the hyperspectrally imagedobjects in the sample of matter.

There remains a need for a method and system for monitoring andoptimizing the manufacturing processes of agricultural products, such astobacco, via hyperspectral imaging and analysis.

SUMMARY

Disclosed herein is a method and system for monitoring and optimizingthe manufacturing processes of agricultural products, including tobacco,via hyperspectral imaging and analysis. The method and system disclosedherein provide high sensitivity, high resolution, and high speedoperation, in a simple yet highly efficient, cost effective andcommercially applicable manner.

In one aspect, disclosed herein is a method for monitoring themanufacturing of an agricultural product, the method utilizinghyperspectral imaging and comprising: scanning at least one region alonga sample of agricultural product using at least one light source of asingle or different wavelengths; generating hyperspectral images fromthe at least one region; determining a spectral fingerprint for thesample of agricultural product from the hyperspectral images; andcomparing the spectral fingerprint so obtained to a spectral fingerprintdatabase containing a plurality of fingerprints obtained at variouspoints of the manufacturing process, using a computer processor, todetermine which point in the manufacturing process the sample hasprogressed to.

In one form, a method is provided to monitor a manufacturing processbased upon obtaining hyperspectral signatures for the agriculturalmaterial being processed, thus minimizing or eliminating the need forhuman evaluation. To accomplish this, first a standard database iscreated that includes hyperspectral signatures taken at each step of aprocess, such as a tobacco aging or fermentation process. The databaseso obtained is used as a benchmark against which the process will bemonitored at each step of the process. Adjustments can them be made tothe process on a real-time basis, by controlling the processingparameters to ensure the quality of the final product.

In one form, a spectral fingerprint is formed for the sample from thehyperspectral images. One or more features of the spectral fingerprintare correlated to desirable sensory attributes of the sample. A widevariety of physical, chemical, and/or biological properties may bedetermined. Certain forms disclosed herein may be performed in anautomatic on-line manner, via hyperspectral imaging and analysis.

In another form, the method includes scanning multiple regions along thesample of agricultural product using at least one light source of asingle or different wavelengths; and generating hyperspectral imagesfrom the multiple regions.

In another form, the method includes determining a physicochemical codefor the sample.

In still yet another form, the method includes the object of themanufacturing process is to obtain an agricultural product withdesirable sensory attributes. In a further form, the method includesdetermining one or more features of a spectral fingerprint thatcorresponds to the desirable sensory attributes.

In a yet further form, the agricultural product is tobacco and themanufacturing process is a fermentation process. In a still yet furtherform, the method determines the time required to complete thefermentation process for the tobacco sample.

In one form, the agricultural product is tobacco and the manufacturingprocess is a tobacco aging process. In another form, the methoddetermines the time required to complete the tobacco aging process forthe tobacco sample.

In yet another form, the method includes correlating one or morefeatures of the spectral fingerprint of the sample of the agriculturalproduct to the desirable sensory attributes.

In still yet another form, the at least one light source is positionedto minimize the angle of incidence of each beam of light with thesample, the at least one light source including a light source selectedfrom the group consisting of a tungsten light source, a halogen lightsource, a xenon light source, a mercury light source, an ultravioletlight source, and combinations thereof.

In a further form, manufacturing cost is a factor used by the computerprocessor.

In a still further form, method further includes storing data about thespectral fingerprints of the plurality of samples of agriculturalproduct within a computer storage means; and storing at least a portionof at least some of the plurality of samples of agricultural product.

In another aspect, provided is a system for monitoring the manufacturingof an agricultural product.

In yet another aspect, provided is a method for determining the stage ofprocessing for an agricultural product, the method utilizinghyperspectral imaging and comprising: (a) scanning multiple regionsalong a sample of a desirable agricultural product using at least onelight source of different wavelengths; (b) generating hyperspectralimages from the multiple regions; (c) forming a spectral fingerprint forthe sample from the hyperspectral images; and (d) correlating thespectral fingerprint obtained in step (c) to a spectral fingerprintdatabase containing a plurality of fingerprints obtained at variouspoints of processing, using a computer processor, to determine the stageprocessing.

In one form, the method includes (e) storing data about the spectralfingerprint within a computer storage means; and (f) repeating steps(a), (b), (c), and (d) using a plurality of samples.

In still yet another aspect, provided is a system for determining thestage of processing for an agricultural product.

In a further aspect, provided is a method of determining the stage ofprocessing for a product, the method comprising: resolving whether asample meets a desired attribute for the product and if so, applyinghyperspectral imaging analysis and theoretic analysis to establish arelationship P comprising unique spectra of the sample, said uniquespectra comprising at least two spectral elements x and y and valuesthereof; establishing through hyperspectral imaging analysis acharacterization of the sample according to said spectral elements (atleast x and y) of said unique spectra P; and mathematically resolvingfrom said characterizations to determine whether the sample achieves thevalues of said spectral elements of P.

In a still further aspect, provided is a method for controlling amanufacturing process for producing an agricultural product. The methodutilizes hyperspectral imaging and comprises obtaining a sample ofagricultural product undergoing a manufacturing process, themanufacturing process conducted at one or more predetermined processparameters; scanning at least one region along the sample ofagricultural product using at least one light source of a single ordifferent wavelengths; generating hyperspectral images from the at leastone region; determining a spectral fingerprint for the sample ofagricultural product from the hyperspectral images; comparing thespectral fingerprint obtained in step (c) to a spectral fingerprintdatabase containing a plurality of fingerprints obtained at variouspoints of the manufacturing process, using a computer processor, todetermine the stage of processing; and adjusting at least one processparameter to optimize the manufacturing process.

In a still yet further aspect, provided is a method of creating adatabase for controlling a manufacturing process for producing anagricultural product. The method utilizes hyperspectral imaging andincludes the steps of (a) obtaining a dark image and a reference imagefor calibration; (b) analyzing the reference image to obtain calibrationcoefficients; (c) obtaining a hyperspectral image for an agriculturalsample; (d) removing dark values and normalizing the agricultural sampleimage; (e) applying calibration coefficients to compensate forfluctuations in system operating conditions; (f) repeating steps (c)-(e)for all agricultural samples; and (g) storing all hyperspectral samplehypercubes to form the database.

In one form, the computer database is stored in a computer readablemedium.

Certain forms disclosed herein are implemented by performing steps orprocedures, and sub-steps or sub-procedures, in a manner selected fromthe group consisting of manually, semi-automatically, fullyautomatically, and combinations thereof, involving use and operation ofsystem units, system sub-units, devices, assemblies, sub-assemblies,mechanisms, structures, components, and elements, and, peripheralequipment, utilities, accessories, and materials. Moreover, according toactual steps or procedures, sub-steps or sub-procedures, system units,system sub-units, devices, assemblies, sub-assemblies, mechanisms,structures, components, and elements, and, peripheral equipment,utilities, accessories, and materials, used for implementing aparticular form, the steps or procedures, and sub-steps orsub-procedures are performed by using hardware, software, and/or anintegrated combination thereof, and the system units, sub-units,devices, assemblies, sub-assemblies, mechanisms, structures, components,and elements, and peripheral equipment, utilities, accessories, andmaterials, operate by using hardware, software, and/or an integratedcombination thereof.

For example, software used, via an operating system, for implementingcertain forms disclosed herein can include operatively interfaced,integrated, connected, and/or functioning written and/or printed data,in the form of software programs, software routines, softwaresubroutines, software symbolic languages, software code, softwareinstructions or protocols, software algorithms, or a combinationthereof. For example, hardware used for implementing certain formsdisclosed herein can include operatively interfaced, integrated,connected, and/or functioning electrical, electronic and/orelectromechanical system units, sub-units, devices, assemblies,sub-assemblies, mechanisms, structures, components, and elements, and,peripheral equipment, utilities, accessories, and materials, which mayinclude one or more computer chips, integrated circuits, electroniccircuits, electronic sub-circuits, hard-wired electrical circuits, or acombination thereof, involving digital and/or analog operations. Certainforms disclosed herein can be implemented by using an integratedcombination of the just described exemplary software and hardware.

In certain forms disclosed herein, steps or procedures, and sub-steps orsub-procedures can be performed by a data processor, such as a computingplatform, for executing a plurality of instructions. Optionally, thedata processor includes volatile memory for storing instructions and/ordata, and/or includes non-volatile storage, for example, a magnetichard-disk and/or removable media, for storing instructions and/or data.Optionally, certain forms disclosed herein include a network connection.Optionally, certain forms disclosed herein include a display device anda user input device, such as a touch screen device, keyboard and/ormouse.

BRIEF DESCRIPTION OF THE DRAWINGS

The forms disclosed herein are illustrated by way of example, and not byway of limitation, for the case of processing tobacco for use inmanufactured tobacco products in the figures of the accompanyingdrawings and in which like reference numerals refer to similar elementsand in which:

FIG. 1 presents, in block diagram form, a first stage of a system formonitoring the manufacturing of an agricultural product, in accordanceherewith;

FIG. 2 presents, in block diagram form, a second stage of a system formonitoring the manufacturing of an agricultural product, in accordanceherewith;

FIG. 3 presents an example of an implementation of a system for scanningan agricultural product employing hyperspectral imaging and analysis, inaccordance herewith;

FIG. 4 presents a method for analyzing data to create a spectrallibrary, in accordance herewith;

FIG. 5 presents a method for analyzing data to create a spectrallibrary, in accordance herewith;

FIG. 6 presents a plot of intensity versus average spectra wavelengthfor tobacco samples at three stages of processing for a hyperspectralimaging and analysis system using halogen light, in accordance herewith;

FIG. 7 presents a plot of intensity versus average spectra wavelengthfor tobacco samples at three stages of processing for a hyperspectralimaging and analysis system using UV light, in accordance herewith; and

FIG. 8 presents a plot of intensity versus average spectra wavelengthfor multiple samples at three stages of processing for a hyperspectralimaging and analysis system using UV light, in accordance herewith.

DETAILED DESCRIPTION

Various aspects will now be described with reference to specific formsselected for purposes of illustration. It will be appreciated by thoseskilled in the art that the spirit and scope of the apparatus, systemand methods disclosed herein are not limited to the selected forms.Moreover, it is to be noted that the figures provided herein are notdrawn to any particular proportion or scale, and that many variationscan be made to the illustrated forms. Reference is now made to FIGS.1-8, wherein like numerals are used to designate like elementsthroughout.

Each of the following terms written in singular grammatical form: “a,”“an,” and “the,” as used herein, may also refer to, and encompass, aplurality of the stated entity or object, unless otherwise specificallydefined or stated herein, or, unless the context clearly dictatesotherwise. For example, the phrases “a device,” “an assembly,” “amechanism,” “a component,” and “an element,” as used herein, may alsorefer to, and encompass, a plurality of devices, a plurality ofassemblies, a plurality of mechanisms, a plurality of components, and aplurality of elements, respectively.

Each of the following terms: “includes,” “including,” “has,” “having,”“comprises,” and “comprising,” and, their linguistic or grammaticalvariants, derivatives, and/or conjugates, as used herein, means“including, but not limited to.”

It is to be understood that the various forms disclosed herein are notlimited in their application to the details of the order or sequence,and number, of steps or procedures, and sub-steps or sub-procedures, ofoperation or implementation of forms of the method or to the details oftype, composition, construction, arrangement, order and number of thesystem, system sub-units, devices, assemblies, sub-assemblies,mechanisms, structures, components, elements, and configurations, and,peripheral equipment, utilities, accessories, and materials of forms ofthe system, set forth in the following illustrative description,accompanying drawings, and examples, unless otherwise specificallystated herein. The apparatus, systems and methods disclosed herein canbe practiced or implemented according to various other alternative formsand in various other alternative ways, as can be appreciated by thoseskilled in the art.

It is also to be understood that all technical and scientific words,terms, and/or phrases, used herein throughout the present disclosurehave either the identical or similar meaning as commonly understood byone of ordinary skill in the art, unless otherwise specifically definedor stated herein. Phraseology, terminology, and, notation, employedherein throughout the present disclosure are for the purpose ofdescription and should not be regarded as limiting.

Moreover, all technical and scientific words, terms, and/or phrases,introduced, defined, described, and/or exemplified, in the abovesections, are equally or similarly applicable in the illustrativedescription, examples and appended claims.

Steps or procedures, sub-steps or sub-procedures, and, equipment andmaterials, system units, system sub-units, devices, assemblies,sub-assemblies, mechanisms, structures, components, elements, andconfigurations, and, peripheral equipment, utilities, accessories, andmaterials, as well as operation and implementation, of exemplary forms,alternative forms, specific configurations, and, additional and optionalaspects, characteristics, or features, thereof, of the methods, and ofthe systems, disclosed herein, are better understood with reference tothe following illustrative description and accompanying drawings.Throughout the following illustrative description and accompanyingdrawings, same reference notation and terminology (i.e., numbers,letters, and/or symbols), refer to same system units, system sub-units,devices, assemblies, sub-assemblies, mechanisms, structures, components,elements, and configurations, and, peripheral equipment, utilities,accessories, and materials, components, elements, and/or parameters.

As a means of illustration, the system will be described for applicationduring tobacco processing, but substantially the same system could beapplied during the processing of other agricultural products.

The forms disclosed herein are generally focused on the domainsencompassing the manufacturing or processing of tobacco, blendcomponents or samples, and are specifically focused on the domainsencompassing the automatic monitoring of tobacco processing, includingaging or fermentation, etc., performed via hyperspectral imaging andanalysis. However, it should be understood that the forms disclosedherein could be applied to other domains encompassing the manufacturingor processing of tea, fruits, during the production of fruit juices,grapes for the production of wines, as well as a vast array of otheragricultural products.

As may be appreciated, the systems and methods described herein havemultiple utilities. With regard to tobacco processing, in one form,tobacco samples may be monitored during a particular process, such asaging or fermentation, to assess their progress and adjust processparameters, either manually or automatically, to achieve an optimumresult.

In hyperspectral imaging, a field of view of a sample is scanned andimaged while the sample is exposed to electromagnetic radiation. Duringthe hyperspectral scanning and imaging there is generated and collectedrelatively large numbers of multiple spectral images, one-at-a-time,but, in an extremely fast sequential manner of the objects emittingelectromagnetic radiation at a plurality of wavelengths and frequencies,where the wavelengths and frequencies are associated with differentselected portions or bands of an entire hyperspectrum emitted by theobjects. A hyperspectral imaging and analysis system can be operated inan extremely rapid manner for providing exceptionally highly resolvedspectral and spatial data and information of an imaged sample of matter,with high accuracy and high precision, which are fundamentallyunattainable by using standard spectral imaging and analysis.

In general, when electromagnetic radiation in the form of light, such asthat used during hyperspectral imaging, is incident upon an object, theelectromagnetic radiation is affected by one or more of the physical,chemical, and/or biological species or components making up the object,by any combination of electromagnetic radiation absorption, diffusion,reflection, diffraction, scattering, and/or transmission, mechanisms.Moreover, an object whose composition includes organic chemical speciesor components, ordinarily exhibits some degree of fluorescent and/orphosphorescent properties, when illuminated by some type ofelectromagnetic radiation or light, such as ultra-violet (UV), visible(VIS), or infrared (IR), types of light. The affected electromagneticradiation, in the form of diffused, reflected, diffracted, scattered,and/or transmitted, electromagnetic radiation emitted by the object isdirectly and uniquely related to the physical, chemical, and/orbiological properties of the object, in general, and of the chemicalspecies or components making up the object, in particular, and thereforerepresents a unique spectral fingerprint or signature pattern type ofidentification and characterization of the object.

A typical spectral imaging system consists of an automated measurementsystem and corresponding analysis software. The automated measurementsystem includes optics, mechanics, electronics, and peripheral hardwareand software, for irradiating, typically using an illuminating source, ascene or sample, followed by measuring and collecting light emitted, forexample, by fluorescence, from objects in the scene or sample, and forapplying calibration techniques best suited for extracting desiredresults from the measurements. Analysis software includes software andmathematical algorithms for analyzing, displaying, and presenting,useful results about the objects in the scene or sample in a meaningfulway.

The hyperspectral image of a scene or a sample could be obtained fromcommercially available hyperspectral imaging cameras from Surface OpticsCorporation of San Diego, Calif., among others, or custom builtaccording to the user needs.

Hyperspectral imaging can be thought of as a combination of spectroscopyand imaging. In spectroscopy a spectra is collected at a single point.Spectra contain information about the chemical composition and materialproperties of a sample, and consist of a continuum of values thatcorrespond to measurements at different wavelengths of light. Incontrast, traditional cameras collect data of thousands of points. Eachpoint or pixel contains one value (black and white image) or threevalues for a color image, corresponding to colors, red, green, and blue.Hyperspectral cameras combine the spectral resolution of spectroscopyand the spatial resolution of cameras. They create images with thousandsof pixels that contain an array of values corresponding to lightmeasurements at different wavelengths. Or, in other words, the data ateach pixel is a spectrum. Together the pixels and the correspondingspectra create a multi-dimensional image cube. The amount of informationcontained in an image cube is immense, and provides a very detaileddescription of the underlying sample.

Each spectral image is a three dimensional data set of voxels (volume ofpixels) in which two dimensions are spatial coordinates or position, (x,y), in an object and the third dimension is the wavelength, (λ), of theemitted light of the object, such that coordinates of each voxel in aspectral image may be represented as (x, y, λ). Any particularwavelength, (λ), of imaged light of the object is associated with a setof spectral images each featuring spectral fingerprints of the object intwo dimensions, for example, along the x and y directions, wherebyvoxels having that value of wavelength constitute the pixels of amonochromatic image of the object at that wavelength. Each spectralimage, featuring a range of wavelengths of imaged light of the object isanalyzed to produce a two dimensional map of one or more physicochemicalproperties, for example, geometrical shape, form, or configuration, anddimensions, and/or chemical composition, of the object and/or ofcomponents of the object, in a scene or sample.

Spectral profiles treat image cubes as a collection of spectra andprovide a detailed and comprehensive description of the image cube as awhole. They use a set of characteristic spectra and their relativeoccurrences within an image cube to summarize the material compositionof the sample. The number of characteristic spectra extracted willdepend upon the variability in the material, and normally range from afew to a few dozen. Spectral profiles are created by matching eachspectra in an image cube to a characteristic spectra. The number ofspectra matched to each characteristic spectra are counted andnormalized to create the spectral profile of an image cube. Thus,spectral profiles can be thought of as a fingerprint derived from thehyperspectral image cube of the tobacco sample.

In hyperspectral imaging, multiple images of each object are generatedfrom object emitted electromagnetic radiation having wavelengths andfrequencies associated with different selected parts or bands of anentire spectrum emitted by the object. For example, hyperspectral imagesof an object are generated from object emitted electromagnetic radiationhaving wavelengths and frequencies associated with one or more of thefollowing bands of an entire spectrum emitted by the object: the visibleband, spanning the wavelength range of about 400-700 nanometers, theinfra-red band, spanning the wavelength range of about 700-3000nanometers, and the deep infra-red band, spanning the wavelength rangeof about 3-12 microns. If proper wavelengths and wavelength ranges areused during hyperspectral imaging, data and information of thehyperspectral images could be optimally used for detecting and analyzingby identifying, discriminating, classifying, and quantifying, the imagedobjects and/or materials, for example, by analyzing different signaturespectra present in pixels of the hyperspectral images.

A high speed hyperspectral imaging system is often required fordifferent types of repeatable and non-repeatable chemical and physicalprocesses taking place during the sub-100 millisecond time scale, whichcannot, therefore, be studied using regular hyperspectral imagingtechniques. Combustion reactions, impulse spectro-electrochemicalexperiments, and inelastic polymer deformations, are examples of suchprocesses. Remote sensing of objects in distant scenes from rapidlymoving platforms, for example, satellites and airplanes, is anotherexample of a quickly changing observable that is often impossible torepeat, and therefore requires high speed hyperspectral imaging.

Disclosed herein, is a method, and system for monitoring themanufacturing of an agricultural product, such as tobacco, viahyperspectral imaging and analysis. In certain forms thereof, providedare methodologies, protocols, procedures and equipment that are highlyaccurate and highly precise, in that they are reproducible and robust,when evaluating agricultural products, such as tobacco. The testingmethodologies disclosed herein exhibit high sensitivity, highresolution, and high speed during automatic on-line operation.

Certain forms disclosed herein are specifically focused on the domainencompassing measuring, analyzing, and determining, micro scaleproperties, characteristics, features, and parameters of agriculturalproducts, such as tobacco, generally with respect to individual tobaccosamples, and specifically with respect to single or individual tobaccoleaves contained within the tobacco samples, and more specifically withrespect to a wide variety of numerous possible physical, chemical,and/or biological properties, characteristics, features, and parametersof single or individual tobacco leaves contained within a given tobaccobale, lot or sample. In one form, provided is an automatic on-lineprocess monitoring system employing hyperspectral imaging and analysis.

Certain forms disclosed herein use what will be referred to as“hyperspectrally detectable and classifiable codes.” As used herein, a“hyperspectrally detectable and classifiable code” is a micro scaleproperty, characteristic, feature, or parameter of a particular bulkagricultural product, such as a tobacco sample, which is hyperspectrallydetectable by hyperspectral imaging and analysis in a manner that theresulting hyperspectral data and information, for example, hyperspectral“fingerprint” or “signature” patterns are usable for classifying atleast part of a single or individual tobacco leaf contained within thatparticular tobacco sample. In turn, the classified part of the single orindividual tobacco leaf contained within that particular tobacco sampleis usable as part of a procedure for monitoring tobacco processing andmay also be used to propose or make process adjustments to achievedesirable results.

Accordingly, a “hyperspectrally detectable and classifiable code” isdefined, generally with respect to a particular individual agriculturalproduct, such as a tobacco sample, and specifically with respect to asingle or individual tobacco leaf contained within the particulartobacco sample, and more specifically with respect to a physical,chemical, and/or biological property, characteristic, feature, orparameter, of that single or individual tobacco leaf contained withinthat particular tobacco sample. The hyperspectrally detectable andclassifiable codes are usable as part of a procedure for (uniquely andunambiguously) monitoring the processing or manufacturing of anagricultural product, such as tobacco.

Primary examples of micro scale testing for generating hyperspectrallydetectable and classifiable codes, include: physical(geometrical/morphological) shape or form and size dimensions of singleor individual tobacco leaves; coloring of single or individual tobaccoleaves; moisture (water) content of, or within, single or individualtobacco leaves; type, distribution, and compositional make-up, of(organic and inorganic) chemical species or components, of single orindividual tobacco leaves; types, distribution, and compositionalmake-up, of possible unknown or foreign (physical, chemical, and/orbiological) matter or species and aspects thereof on, and/or within,single or individual tobacco leaves; activity and/or reactivity ofsingle or individual tobacco leaves in response to physical stimuli oreffects, such as exposure to electromagnetic radiation; activity and/orreactivity of single or individual tobacco leaves in response tochemical stimuli or effects, such as exposure to aqueous liquids or tonon-aqueous (organic based) liquids; and activity and/or reactivity ofsingle or individual tobacco leaves in response to biological stimuli oreffects, such as exposure to biological organisms; physical(geometrical/morphological) shape or form and size dimensions of singleor individual tobacco leaves; coloring of single or individual tobaccoleaves; moisture content of, or within, single or individual tobaccoleaves; types, distribution, and compositional make-up, of (organic andinorganic) chemical species or components, of single or individualtobacco leaves; types, distribution and compositional make-up ofpossible unknown or foreign (physical, chemical, and/or biological)matter or species and aspects thereof on, and/or within, single orindividual tobacco leaves; activity and/or reactivity of single orindividual tobacco leaves in response to physical stimuli or effects,such as exposure to electromagnetic radiation; activity and/orreactivity of single or individual tobacco leaves in response tochemical stimuli or effects (such as exposure to aqueous (water based)liquids or to non-aqueous (organic based) liquids); and activity and/orreactivity of single or individual tobacco leaves in response tobiological stimuli or effects, such as exposure to biological organisms.

Accordingly, provided is a method for monitoring the manufacturing orprocessing of an agricultural product, the method utilizinghyperspectral imaging and comprising: scanning at least one region alonga sample of agricultural product using at least one light source of asingle or different wavelengths; generating hyperspectral images fromthe at least one region; determining a spectral fingerprint for thesample of agricultural product from the hyperspectral images; andcomparing the spectral fingerprint so obtained to a spectral fingerprintdatabase containing a plurality of fingerprints obtained at variouspoints of the manufacturing process, using a computer processor, todetermine which point in the manufacturing process the sample hasprogressed to.

The method is based upon obtaining hyperspectral signatures for theagricultural material being processed to minimize or eliminate the needfor human evaluation during processing. To accomplish this, first astandard database is created that includes hyperspectral signaturestaken at each step of a process, such as a tobacco aging or fermentationprocess. The database so obtained is used as a benchmark against whichthe process will be monitored at each step or stage of the process.Adjustments can them be made to the process on a real-time basis, bycontrolling the processing parameters to ensure the quality of the finalproduct.

The method may comprise scanning multiple regions along the sample ofagricultural product using at least one light source of a single ordifferent wavelengths; and generating hyperspectral images from themultiple regions. The method may further comprise determining a code forthe sample.

The agricultural product may comprise tobacco. The tobacco may be in theform of a sample. At least one light source may be positioned tominimize the angle of incidence of each beam of light with the bale oftobacco. Cost of the samples being processed may be a factor used by thecomputer processor in monitoring and process adjustment.

The method may further comprise repeating the steps of scanning at leastone region along a sample of agricultural product using at least onelight source of different wavelengths, generating hyperspectral imagesfrom the at least one region, and forming a spectral fingerprint for thesample of agricultural product from the hyperspectral images, for aplurality of samples of agricultural product during the processing ofthe agricultural product.

The method may further comprise storing spectral fingerprints data froma plurality of samples of agricultural product taken at various stagesof processing within computer storage means to form a process database.

Further provided is a system monitoring the manufacturing or processingof an agricultural product, according to the methods described above.

Also provided is a method for determining the stage of processing for anagricultural product. The method utilizes hyperspectral imaging andincludes the steps of scanning multiple regions along a sample of adesirable agricultural product using at least one light source ofdifferent wavelengths; generating hyperspectral images from the multipleregions; forming a spectral fingerprint for the sample from thehyperspectral images; and correlating the spectral fingerprint soobtained to a spectral fingerprint database containing a plurality offingerprints obtained at various points of processing, using a computerprocessor, to determine the stage of processing.

The method may further comprise storing data about the spectralfingerprint within a computer storage means, and repeating the steps ofscanning multiple regions along a sample of a desirable agriculturalproduct using at least one light source of a single or differentwavelengths, generating hyperspectral images from the multiple regions,forming a spectral fingerprint for the sample from the hyperspectralimages, storing data about the spectral fingerprint within a computerstorage means, using a plurality of desirable agricultural products. Thedesirable agricultural product may be an unprocessed, semi-processed orfully processed agricultural product, such as tobacco.

While the invention is described in detail for the case of processingtobacco, it should be understood that tobacco is used only to illustratethe methods and systems contemplated herein and not so as to limit theapplication of the methods and systems described herein. Referring toFIGS. 1 and 2, disclosed herein is a method and system, for monitoringthe manufacturing or processing of an agricultural product 100 todetermine the sensory attributes 102, which can be used to assess thestage of processing utilizing process information 104. The system 100uses spectral fingerprints 106 and 124 obtained by hyperspectral imagingsystem 110. Each spectral fingerprint 106 or 124 gives a measure of thephysical and chemical characteristics of the tobacco sample 108 or 122(or other agricultural raw material). The physical and chemicalcharacteristics determine the sensory attributes and stage of processingof the different tobacco samples 108 or 122. By building a database ofthe spectral fingerprints 106 of various tobacco at different stages ofprocessing, an intelligent system 118 can be built based uponstatistical prediction and/or neural network and artificial intelligencetechniques 120 to develop a system for monitoring the manufacturing orprocessing of an agricultural product 100. The algorithm can beoptimized for cost reduction by including the cost of different tobaccosamples together with the cost to process them, as one of the variablesused in an optimization scheme.

Referring to FIG. 1, in a first stage 101 of the system 100, a databaseis built with existing tobacco samples 108 taken at different stages ofprocessing and the hyperspectral fingerprints 106 and the subjectivesensory attributes 116 of tobacco samples. An intelligent system 118 isbuilt based upon a neural network or an artificial intelligencealgorithm 120 that provides a mapping of the hyperspectral signaturewith the stage of processing and the subjective sensory attributes 116obtained from a sensory panel 120. The costs of individual tobacco andits processing costs can also be used as independent parameters in thealgorithm to optimize the processing scheme for sensory attributes andcost effectiveness. At the end of the first stage 101, there is formed acomposite hyperspectral signature, which can be correlated tosatisfactory sensory attributes.

Referring to FIG. 2, in a second phase 103, spectral fingerprints 124 oftobacco samples 122 are obtained at different levels of processing. Thecost of each of these tobacco samples 122, both initial and cost toprocess to a certain level are also obtained and used as an input to thesystem 100. Intelligent system 114 will determine, using the expertsystem 118 developed in the first phase 101, whether additionalprocessing is required, and whether process parameters requireadjustment to obtain optimal cost and acceptable sensory attributesusing the input parameters, the spectral fingerprints 124, and the costinformation obtained for the samples.

Accordingly, provided is a method for monitoring a manufacturing processof an agricultural product. The method utilizes hyperspectral imagingand includes the steps of scanning at least one region along a sample ofagricultural product using at least one light source of a single ordifferent wavelengths; generating hyperspectral images from the at leastone region; determining a spectral fingerprint for the sample ofagricultural product from the hyperspectral images; and comparing thespectral fingerprint obtained in step (c) to a spectral fingerprintdatabase containing a plurality of fingerprints obtained at variouspoints of the manufacturing process, using a computer processor, todetermine which point in the manufacturing process the sample hasprogressed to. In some forms, one or more features of a spectralfingerprint may be determined that correspond to the desirable sensoryattributes. In some forms, one or more features of the spectralfingerprint of the sample of the agricultural product are correlated tothe desirable sensory attributes.

The method may comprise scanning multiple regions along the sample ofagricultural product using at least one light source of a single ordifferent wavelengths; and generating hyperspectral images from themultiple regions. The method may further comprise determining a code forthe sample.

The agricultural product may comprise tobacco. The tobacco may be in theform of a bale, lot or sample. At least one light source may bepositioned to minimize the angle of incidence of each beam of light withthe sample being imaged.

In some forms, cost of the samples and/or its processing may be a factorused by the computer processor.

In some forms, the agricultural product is tobacco and the manufacturingprocess is a fermentation process. In some forms, the method determinesthe time required to complete the fermentation process for the tobaccosample.

In some forms, the agricultural product is tobacco and the manufacturingprocess is a tobacco aging process. In some forms, the method determinesthe time required to complete the tobacco aging process for the tobaccosample.

As may be appreciated, the method disclosed herein may be computerimplemented. In some forms, data about the spectral fingerprints of theplurality of samples of agricultural product are stored within acomputer storage means.

Also provided is a method for determining the stage of processing for anagricultural product utilizing hyperspectral imaging. The methodincludes the steps of scanning multiple regions along a sample of adesirable agricultural product using at least one light source ofdifferent wavelengths; generating hyperspectral images from the multipleregions; forming a spectral fingerprint for the sample from thehyperspectral images; and correlating the spectral fingerprint obtainedin step (c) to a spectral fingerprint database containing a plurality offingerprints obtained at various points of processing, using a computerprocessor, to determine the stage of processing.

In another aspect, provided is a method of determining the stage ofprocessing for a product. The method includes the steps of resolvingwhether a sample meets a desired attribute for the product and if so,applying hyperspectral imaging analysis and theoretic analysis toestablish a relationship P comprising unique spectra of the sample, saidunique spectra comprising at least two spectral elements x and y andvalues thereof; establishing through hyperspectral imaging analysis acharacterization of the sample according to said spectral elements (atleast x and y) of said unique spectra P; and mathematically resolvingfrom said characterizations to determine whether the sample achieves thevalues of said spectral elements of P.

Referring now to FIG. 3, a system 10 for monitoring a manufacturingprocess of an agricultural product P employing hyperspectral imaging andanalysis is shown in schematic form. The system 10 includes at least onelight source 12 for providing a beam of light. As shown, the at leastone light source 12 may be mounted on an arm 14 for positioning at leastone light source 12 in proximity to the agricultural product (notshown), which may be positioned on platform 50. In one form, arm 14 ismounted to frame 16 of cabinet 20 and may be either fixed thereto ormoveably positionable, as will be described hereinbelow. As shown inFIG. 3, a second light source 18 may also be provided and mounted toframe 16 of cabinet 20 or, optionally, to an arm (not shown), which inturn is mounted to frame 16 of cabinet 20.

In one form, the at least one light source 12 for providing a beam oflight of different wavelengths comprises a tungsten, halogen or a xenonlight source. In another form, the at least one light source 12 forproviding a beam of light comprises a mercury light source. In yetanother form, the at least one light source or the second light source18 comprises an ultraviolet light source for use in providing a chemicalsignature of the agricultural product P. This optional ultraviolet lightsource adds an additional media of classification that provides a betterunderstanding of an agricultural product's characteristics. In still yetanother form, the at least one light source 12 comprises a xenon lightsource, the second light source 18 comprises a mercury light source anda third light source (not shown) comprises an ultraviolet light source.

In one form, the at least one light source 12 and/or the second lightsource 18 may be positioned to minimize the angle of incidence of a beamof light with the agricultural product P.

In order to segregate ambient light from the light provided by system10, walls (not shown) may be added to cabinet 20 to form an enclosure toprovide a dark-room-like environment for system 10.

The hyperspectral image of a scene or a sample is obtained usinghyperspectral imaging camera 24.

Referring still to FIG. 3, a computer 40 having a processor capable ofrapidly handling system data is provided and programmed to compare thedetected component wavelengths to a database of previously analyzedagricultural products to identify agricultural product P. Computer 40may be a personal computer having an Intel® Core™ 2 Quad or otherprocessor. Computer 40 may also control the operation of the system 10and the positioning of the head unit 20 about agricultural product P. Adevice 34 for providing an uninterrupted source of power to computer 40may be provided, such devices readily available from a variety ofcommercial sources. As is conventional, computer 40 may also include akeyboard 36 and monitor 38 to enable input and system monitoring by auser U. A regulated power supply 34 may be provided to assure that atightly controlled source of power is supplied to system 10.

Test results are based on the scanning and counting of individualsamples, each comprising dozens of scans, and each sample classifiedusing spectral band features, spectral finger prints (SFP), majorspectral representative components, purity and quality of each majorcompound (component, SFP), relative quantity of each SFP and,optionally, crystallization and morphological features.

A plurality of samples of agricultural products, such as tobacco samplesat various stages of processing is scanned. As indicated, foragricultural products such as tobacco, a significant number of samplesshould be scanned in order that the impact of sample variability isreduced. In practice, it has been observed that the impact due to thisvariability can be reduced when the number of samples N is about 5 toabout 25. However, by carefully selecting representative samples, fewersamples could be used to incorporate all the normal variations observedin processing a particular product. Applying this technique to tobacco,tobacco samples may be scanned using xenon and/or mercury and/ortungsten, and/or halogen light sources and an optional ultraviolet lightsource may be used for chemical signature classification.

In operation, the light source(s) is activated (one, two, three or morespot lights in parallel to the region of interest (ROI)), permittingredundant data to be gathered. A plurality of regions of interest (suchas by way of example, but not of limitation, a 20 cm×20 cm area for eachROI) is scanned for each sample to provide one, two, three or morehyperspectral images. Scanning is performed and the reflection spectralsignature and optional fluorescence spectral chemical signaturereceived. The images are then saved and a database (including labelsidentifying the particular sample and/or lot) is thus formed from thecombined information obtained for the N samples

During scanning, the hyperspectral camera system provides a threedimensional hyperspectral image cube. The image cube, which may be, byway of example, but not of limitation, on the order of about a 696 pixelby 520 pixel array. Such a picture or frame would thus contain 361,920pixels. As may be appreciated by those skilled in the art, each pixelmay contain about 128, 256, 500 or more spectra points at differentwavelengths for an agricultural product such as tobacco.

Referring now to FIG. 4, an algorithm 200 for use in the system andmethods described herein will now be disclosed. In step 210, a darkimage and reference image are obtained for use in system calibration. Instep 220, the reference image is analyzed and calibration coefficientsare obtained. In step 230, a hyperspectral image of a tobacco sample isobtained. In step 240, using the information obtained duringcalibration, dark values are removed and the sample image normalized. Instep 250, calibration coefficients are applied to compensate forfluctuations in operating conditions (e.g., light intensity, ambientconditions, etc.). In step 260, steps 230-250 are repeated for allsamples and the data so obtained is added in step 270 to the database ofspectral hypercubes (whole dataset). It should be understood that thealgorithm for creating the database as illustrated in FIG. 4 isreferenced to tobacco samples by way of illustration only and not aslimiting. The same steps could be used to create the whole spectraldatabase for application in other agricultural products like tea, fruitsgrapes or other products. The result is 350, a spectral library for allthe samples that could be used to assess and monitor processing of theagricultural product, which contains the spectral fingerprints of thedataset found in step 340, and the unique spectra found in step 330.

Referring now to FIG. 5, a method for analyzing data to create aspectral library of samples 300, in accordance herewith is shown.Spectral library 300 is formed by obtaining a dataset in step 310. Instep 320, the dataset is preprocessed. Unique spectra, indicative of thedataset, are identified in step 330. In step 340, spectral distributions(spectral fingerprints) are found for each of the samples using theunique spectra found in step 330.

In some forms, following imaging, several data processing routines areperformed to reduce noise, increase consistency, enhance spectra forfeature extraction, and reduce computation time. The data processingroutines are summarized below.

Spectral binning is used to produce more consistent signals, reduce filesize, and decrease processing time. Spectral binning is an operationperformed on each spectra of the image cube, and consists of summationof adjacent wavelengths to produce a down-sampled spectra. Down-sampledspectra have less resolution, but increased signal to noise ratio. Asampling rate was chosen that creates a signal with maximum compressionand minimal loss of fidelity. This is followed by median filtering toreduce the noise and increase the signal to noise ratio.

Spatial Binning is applied to the image cube, and consists ofdown-sampling via summation of adjacent pixels. Pixels contain spectra,so spatial binning results in summation of adjacent spectra. Thisincreases signal to noise ratio, reduces file size, and decreasesprocessing time. Information loss is minimal because the camera isselected to provide a high resolution. Summation allows for each pixel'sspectra to contribute to the down-sampled spectra, and adjacent pixelsare usually from the same part of the leaf, which tend to have similarspectra. Similar effects may be achieved by using a Gaussian filter toincrease spatial coherency and to reduce “salt and pepper” pixelclassification. A Gaussian filter may be useful when spatial reductionis not desirable.

In some forms, image correction may be performed by first collectingdark images twice daily. These are then used during processing toestimate and remove sensor noise. The dark image processing procedureconsists of two steps. Dark images' spectra, ^(→)d_(j)∈D_(i) are firstbinned to be consistent with other images cubes. Then the spectral mean,^(→)d^(mean;i) (Eq. 1) and used to estimate sensor noise.

$\begin{matrix}{{\overset{arrow}{s}}_{i}^{mean} = {{\sum\limits^{\forall{{\overset{arrow}{s}}_{j} \in S_{i}}}{\overset{arrow}{s}}_{j}}}} & (1)\end{matrix}$

During data processing ^(→)d^(mean;)i is removed from each spectra ofeach sample by subtraction. This is a standard method of removing sensornoise.

Reference image cubes, R_(i) are collected twice-daily and containspectra that can be used to measure and correct lightinginconsistencies. Applying a correction based on the reference image willalso eliminate any sensor drift or variation over time. This step isvery important as any changes in either the lighting conditions or thesensor response drift will adversely affect the system performance.Also, pixels of shadow have low signal to noise ratio and should beeliminated.

Most signals tend to have a maximum peak created by the spectralsignature of the light whose value is proportional to the amount oflight hitting the sensor, and the sensor's response. For this reasonshadow detection is applied using maximum peak thresholding.

${pixel} = \{ \begin{matrix}{{{not}\mspace{14mu} {shadow}\text{:}\mspace{14mu} {\max( {\overset{arrow}{s}}_{i} )}} \geq {thresh}} \\{{{shadow}\text{:}\mspace{14mu} {\max( {\overset{arrow}{s}}_{i} )}} \geq {thresh}}\end{matrix} $

where max(^(→)si) returns the maximum component of the spectra, ^(→)si.

Spectra with a max peak less than a user-defined threshold are tagged asshadow spectra, and ignored during spatial binning, spectral binning,local spectra extraction, and spectra matching.

Image cubes of tobacco samples contain thousands of spectra, many ofwhich are nearly identical and correspond to similar material propertiesand sensorial effects. The information contained in the image cube canbe summarized as a spectral profile using a set of characteristicspectra and their occurrence rate within a sample. Construction of aspectral profile consists of two primary steps.

-   1. Spectra Extraction—finding characteristic spectra.-   2. Spectra Matching—matching an image cube's spectra to    characteristic spectra.

The first step of building a spectral profile is to create a set ofcharacteristic spectra often called end-members. End-members are oftenmanually selected by choosing pixels that are known to correspond to aspecific class or contain a unique material. For many agriculturalproducts, including tobacco, distinct characteristics can be verydifficult to detect manually and class specific spectra arenon-existent. In such cases an automated spectra extraction technique ispreferable.

Spectra extraction differs from other automated end-member extractiontechniques in that it divides the spectral feature space using an evenlyspaced grid. Spectra extraction finds all spectra in a data set that aremore dissimilar than a user-defined threshold, α*. The assumption isthat if two spectra are more similar than α* they represent identicalmaterials and can be considered duplicates. By finding all dissimilarspectra in a data set, all unique materials can be found. Otherautomated spectra extraction algorithms are often associated withun-mixing models, which assume individual pixels contain a combinationof unique spectral signatures from multiple materials. SequentialMaximum Angle Convex Cone (SMACC) and Support Vector Machine-BasedEnd-member extraction are two examples. The technique disclosed hereinwas chosen for simplicity. Un-mixing models may not be appropriate forsmaller scale agricultural imaging, where individual pixel size is inmillimeters as opposed to aerial imaging, where individual pixel size isusually in meters. The spectra extraction procedure disclosed hereinfirst analyzes image cubes independently in a step called local spectraextraction. The results are then combined during global spectraextraction. Extraction in this order decreases processing time andallows for outliers of individual image cubes to be eliminated.

Once the characteristic spectra of the data set have been extracted,spectra matching step is applied on each image cube. Each^(→)s_(j)∈S_(i) is matched with the most similar ^(→)c_(k)∈C_(all). Asan image cube is analyzed, a tally of the number of matches for each^(→)c_(k)∈C_(all) is kept as a spectral profile, ^(→)p_(i). Eachcomponent of ^(→)p_(i) corresponds to the number of matches for a single^(→)c_(k)∈C_(all). Once all ^(→)s_(j)∈S_(i) have been matched ^(→)p_(i)is normalized and represents the percent occurrence rate of each^(→)c_(k)∈C_(all) in Si.

The inclusion of unidentified pixels can be advantageous for certainscenarios such as when tobacco samples are contaminated with non-tobaccomaterial, if shadow detection is unreliable, or if only a selected fewspectra should be included in the spectral profiles. Rather than forcinga match with the most similar ^(→)c_(k)∈C_(all), Spectra that are moredissimilar than α** to all ^(→)c_(k)∈C_(all) are counted asunidentified. Unidentified pixels do not contribute to the spectralprofile. α** is a user-defined parameter, and larger values of α** willallow more dissimilar spectra to match to ^(→)c_(k)∈C_(all), while smallvalues will allow matches with only similar spectra. Setting a high α*will force a match with the closest ^(→)c_(k)∈C_(all).

Since previous hyperspectral image classification problems have focusedon pixel by pixel classification, spectra matching is often the goal ofhyperspectral image analysis. These applications often make use ofmachine learning algorithms such as support vector machines and decisiontrees to classify spectra. Matching in this manner requires a trainingset of characteristic spectra which is not practical. Fully automatedtechniques are preferable, and also may use spectral feature fitting(SFF). SFF is designed to distinguish between spectra using specificfeatures of a spectra. While SFF can achieve successful results, SAM isa more appropriate measure, since it is intended to find the similaritybetween two spectra using all the bands as confirmed by the results.

The goal of feature selection is to choose a subset of features capableof summarizing the data with little or no information loss. It isapplied before classification to avoid dimensionality, which oftenreduces classification performance. Spectral profiles can containredundant features, particularly when the spectra extraction threshold,α* is low. Experimental work suggests that selection of an appropriateα* combined with the ability of support vector machines (SVM) to handleredundant and/or uninformative features eliminates the need for afeature selection step. However in some cases we found that choosing theoptimal subset of features using the Jeffreys-Matusita Distance as aninformation measure was effective.

In some forms, classification may be conducted using well-knownprocedures, by way of example and not of limitation, discriminantanalysis, SVM, neural networks, etc., as those skilled in the art willrecognize.

As may be appreciated, the amount of raw data is very large, due to thelarge number of pixels existing within a particular image cube. Aseparate spectral is obtained for each pixel. Advantageously, the methoddisclosed herein identifies a set of characteristic spectra for thewhole dataset. The composition of the spectra provides a signature forthe sample, with similar samples having similar signatures orfingerprints. Unique spectral fingerprints are then identified for eachtobacco sample and for each stage of processing.

The spectral library or database of the samples developed in FIG. 5 maybe used to gauge the progression of a manufacturing process, such astobacco ageing or fermentation. This information may also be used in aclosed system capable of making adjustments to process parameters, suchas temperature, air flow, humidity, etc. to optimize desirable sensoryattributes.

Advantageously, the method and system disclosed herein provides achemical imaging platform that enables speed and exceptionally highsensitivity, thus accurate and non-destructive nature, whereinmeasurement time is greatly reduced. This enables the tracking of themonitored material with high repeatability.

The method and system disclosed herein provides a highly sensitive hyperspectral imaging analyzer with co-sharing database capabilities. Thedigitized highly sensitive imaging system disclosed herein enables theimaging of materials components, more detailed observation and providesmore specific, sensitive, accurate and repeatable measurements. This maybe achieved using advanced image recognition technologies, as well asadaptive and collaborative data bases and statistical and optimizationalgorithms.

The method and system disclosed herein is capable of characterizing thecomposition of inorganic, organic, and chemical particulate mattersuspended in agricultural products such as tobacco. The instrument scansleaf or other samples, analyses the scanned samples' wavelengthsignature, performing trends analysis and compares the gathered data todata bases that may, in one form, be continuously updated. The systemthen generates reports for system users. A remote network may beprovided to support the gathering of data for integrated databaseconstruction as well as to support remote professional personnel in realtime.

Advantageously, the proposed method requires no sample preparation. Inoperation, linear calibration plots in the ppm range are obtained formono-component contamination and for simple compound mixtures in thismatrix. Non-contact, non-destructive, near real time on-line, automatedphysicochemical imaging, classification and analysis of a sample oftobacco leaves or other agricultural products is provided without theneed for consumable materials for sample processing. The system operatesusing algorithms and software packages, together with unique ultra-highresolution optical components and a two-dimensional sample positioningof regions of interest for generating, for example, five dimensional(5D) spectral images of the tobacco sample under analysis.

All or a portion of the devices and subsystems of the exemplary formscan be conveniently implemented using one or more general purposecomputer systems, microprocessors, digital signal processors,microcontrollers, and the like, programmed according to the teachings ofthe exemplary forms disclosed herein, as will be appreciated by thoseskilled in the computer and software arts.

In view thereof, in one form there is provided a computer programproduct for monitoring a manufacturing process of an agriculturalproduct, the computer program structured and arranged to determine oneor more features of a spectral fingerprint that correspond to desirablesensory attributes, and/or determining processing parameters, includingone or more computer readable instructions embedded on a tangiblecomputer readable medium and configured to cause one or more computerprocessors to perform the steps described above and transmittinginformation relating to the steps described above over a communicationslink.

Appropriate software can be readily prepared by programmers of ordinaryskill based on the teachings of the exemplary forms, as will beappreciated by those skilled in the software art. Further, the devicesand subsystems of the exemplary forms can be implemented on the WorldWide Web. In addition, the devices and subsystems of the exemplary formscan be implemented by the preparation of application-specific integratedcircuits or by interconnecting an appropriate network of conventionalcomponent circuits, as will be appreciated by those skilled in theelectrical art(s). Thus, the exemplary forms are not limited to anyspecific combination of hardware circuitry and/or software.

Stored on any one or on a combination of computer readable media, theexemplary forms disclosed herein can include software for controllingthe devices and subsystems of the exemplary forms, for driving thedevices and subsystems of the exemplary forms, for enabling the devicesand subsystems of the exemplary forms to interact with a human user, andthe like. Such software can include, but is not limited to, devicedrivers, firmware, operating systems, development tools, applicationssoftware, and the like. Such computer readable media further can includethe computer program product of a form disclosed herein for performingall or a portion (if processing is distributed) of the processingperformed in implementing the methods disclosed herein. Computer codedevices of the exemplary forms disclosed herein can include any suitableinterpretable or executable code mechanism, including but not limited toscripts, interpretable programs, dynamic link libraries (DLLs), Javaclasses and applets, complete executable programs, Common Object RequestBroker Architecture (CORBA) objects, and the like. Moreover, parts ofthe processing of the exemplary forms disclosed herein can bedistributed for better performance, reliability, cost, and the like.

As stated above, the devices and subsystems of the exemplary forms caninclude computer readable medium or memories for holding instructionsprogrammed according to the forms disclosed herein and for holding datastructures, tables, records, and/or other data described herein.Computer readable medium can include any suitable medium thatparticipates in providing instructions to a processor for execution.Such a medium can take many forms, including but not limited to,non-volatile media, volatile media, transmission media, and the like.Non-volatile media can include, for example, optical or magnetic disks,magneto-optical disks, and the like. Volatile media can include dynamicmemories, and the like. Transmission media can include coaxial cables,copper wire, fiber optics, and the like. Transmission media also cantake the form of acoustic, optical, electromagnetic waves, and the like,such as those generated during radio frequency (RF) communications,infrared (IR) data communications, and the like. Common forms ofcomputer-readable media can include, for example, a floppy disk, aflexible disk, hard disk, magnetic tape, any other suitable magneticmedium, a CD-ROM, CDRW, DVD, any other suitable optical medium, punchcards, paper tape, optical mark sheets, any other suitable physicalmedium with patterns of holes or other optically recognizable indicia, aRAM, a PROM, an EPROM, a FLASH-EPROM, any other suitable memory chip orcartridge, a carrier wave or any other suitable medium from which acomputer can read.

The forms disclosed herein, as illustratively described and exemplifiedhereinabove, have several beneficial and advantageous aspects,characteristics, and features. The forms disclosed herein successfullyaddress and overcome shortcomings and limitations, and widen the scope,of currently known teachings with respect to the processing ofagricultural products such as tobacco.

The forms disclosed herein, provide for processing methodologies(protocols, procedures, equipment) for, by way of example, but not oflimitation, tobacco samples, which are highly accurate and highlyprecise (reproducible, robust). The forms disclosed herein, provide highsensitivity, high resolution, and high speed (fast, at short timescales), during automatic operation, in an optimum and highly efficient(cost effective) commercially applicable manner.

As may be appreciated, the performance of the methods and systemsdisclosed herein is dependent of the number of regions and samplesscanned, image sizes, light sources, filters, light source energystability, etc.

EXAMPLES Example 1

The operation of the system and a method of forming a database will nowbe described by way of this prophetic example.

The system is initiated and the light sources brought up to operatingtemperature. A dark image and reference image are obtained for systemcalibration. The reference image is analyzed and calibrationcoefficients are obtained. A hyperspectral image of a tobacco or otheragricultural sample is obtained.

Using the information obtained during the aforementioned calibration,dark values are removed and the sample image normalized. Calibrationcoefficients are applied to compensate for fluctuations in operatingconditions (e.g., light intensity, ambient conditions, etc.).Hyperspectral images for additional tobacco samples are obtained foradditional samples of interest and all data so obtained is added to thedatabase of spectral hypercubes (whole dataset).

A spectral library is formed from the database of spectral hypercubes byfirst preprocessing the data. Unique spectra, indicative of the datasetare identified and samples mapped to the spectral fingerprints soobtained. The unique spectra are then added to a spectral database ofprocessing parameters and information.

Example 2

When a new batch of agricultural product is to be processed, first, aspectral distribution is obtained for this product based on theteachings of this invention.

A system of the type shown in FIG. 3 was used. The samples employed weretaken at 1) a point prior to processing, at 2) an intermediate stage ofprocessing and at 3) the end of processing. As described herein, aselection algorithm is used, the results of which provide the processingparameters necessary to obtain optimized processing.

Referring now to FIG. 6, a plot of intensity versus average spectrawavelength for tobacco samples at the three different stages ofprocessing is presented for a hyperspectral imaging and analysis systemusing halogen light. As may be seen, excellent resolution was achieved,with each stage of processing discernible from the others.

Example 3

Once again, a system of the type shown in FIG. 3 was used. The samplesemployed were taken at 1) a point prior to processing, at 2) anintermediate stage of processing and at 3) the end of processing. Asdescribed herein, a selection algorithm is used, the results of whichprovide the processing parameters necessary to obtain optimizedprocessing.

Referring now to FIG. 7, a plot of intensity versus average spectrawavelength for tobacco samples at the three different stages ofprocessing is presented for a hyperspectral imaging and analysis systemusing UV light. As may be seen, excellent resolution was achieved, witheach stage of processing discernible from the others.

Example 4

Once again, a system of the type shown in FIG. 3 was used. The samplesemployed were taken at 1) a point prior to processing, at 2) anintermediate stage of processing and at 3) the end of processing. Asdescribed herein, a selection algorithm is used, the results of whichprovide the processing parameters necessary to obtain optimizedprocessing.

Referring now to FIG. 8, a plot of intensity versus average spectrawavelength for tobacco samples at three different stages of processingis presented for a hyperspectral imaging and analysis system using UVlight. Again, as may be seen, excellent resolution was achieved, witheach stage of processing discernible from the others.

As may be appreciated, upon implementation in accordance with theforegoing teachings, the system will lessen or obviate the need to usesensorial panels in the quality control of commercially manufacturedagricultural products.

As used herein the terms “adapted” and “configured” mean that theelement, component, or other subject matter is designed and/or intendedto perform a given function. Thus, the use of the terms “adapted” and“configured” should not be construed to mean that a given element,component, or other subject matter is simply “capable of” performing agiven function but that the element, component, and/or other subjectmatter is specifically selected, created, implemented, utilized,programmed, and/or designed for the purpose of performing the function.It is also within the scope of the present disclosure that elements,components, and/or other recited subject matter that is recited as beingadapted to perform a particular function may additionally oralternatively be described as being configured to perform that function,and vice versa.

Illustrative, non-exclusive examples of systems and methods according tothe present disclosure are presented in the following enumeratedparagraphs. It is within the scope of the present disclosure that anindividual step of a method recited herein, including in the followingenumerated paragraphs, may additionally or alternatively be referred toas a “step for” performing the recited action.

A1. A method for monitoring a manufacturing process of an agriculturalproduct, the method utilizing hyperspectral imaging and comprising: (a)scanning at least one region along a sample of agricultural productusing at least one light source of a single or different wavelengths;(b) generating hyperspectral images from the at least one region; (c)determining a spectral fingerprint for the sample of agriculturalproduct from the hyperspectral images; and (d) comparing the spectralfingerprint obtained in step (c) to a spectral fingerprint databasecontaining a plurality of fingerprints obtained at various points of themanufacturing process, using a computer processor, to determine whichpoint in the manufacturing process the sample has progressed to.

A2. The method of paragraph A1, further comprising: scanning multipleregions along the sample of agricultural product using at least onelight source of a single or different wavelengths; and generatinghyperspectral images from the multiple regions.

A3. The method of paragraph A1, further comprising determining aphysicochemical code for the sample.

A4. The method of paragraph A1, wherein the manufacturing processproduces an agricultural product with desirable sensory attributes.

A5. The method of paragraph A4, further comprising determining one ormore features of a spectral fingerprint that correspond to the desirablesensory attributes.

A6. The method of paragraph A5, wherein the agricultural product istobacco and the manufacturing process is a fermentation process.

A7. The method of paragraph A6, wherein the method determines the timerequired to complete the fermentation process for the tobacco sample.

A8. The method of paragraph A5, wherein the agricultural product istobacco and the manufacturing process is a tobacco aging process.

A9. The method of paragraph A8, wherein the method determines the timerequired to complete the tobacco aging process for the tobacco sample.

A10. The method of paragraph A5, further comprising: correlating one ormore features of the spectral fingerprint of the sample of theagricultural product to the desirable sensory attributes.

A11. The method of paragraph A1, wherein the at least one light sourceis positioned to minimize the angle of incidence of each beam of lightwith the sample.

A12. The method of paragraph A1, wherein manufacturing cost is a factorused by the computer processor in step (d).

A13. The method of paragraph A1, wherein the at least one light sourcefor providing a beam of light comprises a light source selected from thegroup consisting of a tungsten light source, a halogen light source, axenon light source, a mercury light source, an ultraviolet light source,and combinations thereof.

A14. The method of paragraph A1, further comprising repeating steps (a),(b), and (c) for a plurality of samples of agricultural product.

A15. The method of paragraph A14, further comprising, prior to step (d):storing data about the spectral fingerprints of the plurality of samplesof agricultural product within a computer storage means; and storing atleast a portion of at least some of the plurality of samples ofagricultural product.

A16. A system for monitoring the manufacturing of an agriculturalproduct, according to the method of paragraph A1.

B1. A method for determining the stage of processing for an agriculturalproduct, the method utilizing hyperspectral imaging and comprising: (a)scanning multiple regions along a sample of a desirable agriculturalproduct using at least one light source of different wavelengths; (b)generating hyperspectral images from the multiple regions; (c) forming aspectral fingerprint for the sample from the hyperspectral images; and(d) correlating the spectral fingerprint obtained in step (c) to aspectral fingerprint database containing a plurality of fingerprintsobtained at various points of processing, using a computer processor, todetermine the stage of processing.

B2. The method of paragraph B1, further comprising: (e)storing dataabout the spectral fingerprint within a computer storage means; and (f)repeating steps (a), (b), (c), and (d) using a plurality of samples.

B3. A system for determining the stage of processing for an agriculturalproduct, according to the method of paragraph B2.

C1. A method of determining the stage of processing for a product, themethod comprising: resolving whether a sample meets a desired attributefor the product and if so, applying hyperspectral imaging analysis andtheoretic analysis to establish a relationship P comprising uniquespectra of the sample, said unique spectra comprising at least twospectral elements x and y and values thereof; establishing throughhyperspectral imaging analysis a characterization of the sampleaccording to said spectral elements (at least x and y) of said uniquespectra P; and mathematically resolving from said characterizations todetermine whether the sample achieves the values of said spectralelements of P.

D1. A method for controlling a manufacturing process for producing anagricultural product, the method utilizing hyperspectral imaging andcomprising: (a) obtaining a sample of agricultural product undergoing amanufacturing process, the manufacturing process conducted at one ormore predetermined process parameters; (b) scanning at least one regionalong the sample of agricultural product using at least one light sourceof a single or different wavelengths; (c) generating hyperspectralimages from the at least one region; (d) determining a spectralfingerprint for the sample of agricultural product from thehyperspectral images; (e) comparing the spectral fingerprint obtained instep (c) to a spectral fingerprint database containing a plurality offingerprints obtained at various points of the manufacturing process,using a computer processor, to determine the stage of processing; and(e) adjusting at least one process parameter to optimize themanufacturing process.

D2. The method of paragraph D1, wherein the manufacturing processproduces an agricultural product with desirable sensory attributes.

D3. The method of paragraph D2, further comprising determining one ormore features of a spectral fingerprint that correspond to the desirablesensory attributes.

D4. The method of paragraph D1, wherein the agricultural product istobacco and the manufacturing process is a fermentation process.

D5. The method of paragraph D4, wherein the method determines the timerequired to complete the fermentation process for the tobacco sample.

D6. The method of paragraph D1, wherein the agricultural product istobacco and the manufacturing process is a tobacco aging process.

D7. The method of paragraph D6, wherein the method determines the timerequired to complete the tobacco aging process for the tobacco sample.

D8. The method paragraph D2, further comprising: correlating one or morefeatures of the spectral fingerprint of the sample of the agriculturalproduct to the desirable sensory attributes.

D9. The method of paragraph D1, wherein the at least one light source ispositioned to minimize the angle of incidence of each beam of light withthe sample.

D10. The method of paragraph D1, wherein manufacturing cost is a factorused by the computer processor in step (d).

E1. A method of creating a database for controlling a manufacturingprocess for producing an agricultural product, the method utilizinghyperspectral imaging and comprising: (a) obtaining a dark image and areference image for calibration; (b) analyzing the reference image toobtain calibration coefficients; (c) obtaining a hyperspectral image foran agricultural sample; (d) removing dark values and normalizing theagricultural sample image; (e) applying calibration coefficients tocompensate for fluctuations in system operating conditions; (f)repeating steps (c)-(e) for all agricultural samples; and (g) storingall hyperspectral sample hypercubes to form the database.

E2. A computer database stored in a computer readable medium, producedin accordance with paragraph E1.

INDUSTRIAL APPLICABILITY

The systems and methods disclosed herein are applicable to the tobaccoindustry and to other industries wherein agricultural products areselected and/or processed.

It is to be fully understood that certain aspects, characteristics, andfeatures, of the forms disclosed herein, which are illustrativelydescribed and presented in the context or format of a plurality ofseparate forms, may also be illustratively described and presented inany suitable combination or sub-combination in the context or format ofa single form. Conversely, various aspects, characteristics, andfeatures, of the forms disclosed herein, which are illustrativelydescribed and presented in combination or sub-combination in the contextor format of a single form, may also be illustratively described andpresented in the context or format of a plurality of separate forms.

All patents, patent applications, and publications, cited or referred toin this specification are herein incorporated in their entirety byreference into the specification, to the same extent as if eachindividual patent, patent application, or publication, was specificallyand individually indicated to be incorporated herein by reference. Inaddition, citation or identification of any reference in thisspecification shall not be construed or understood as an admission thatsuch reference represents or corresponds to prior art. To the extentthat section headings are used, they should not be construed asnecessarily limiting.

While the forms disclosed herein have been described in connection witha number of exemplary forms, and implementations, the forms disclosedherein are not so limited, but rather cover various modifications, andequivalent arrangements, which fall within the purview of the presentclaims.

What is claimed:
 1. A method for classifying at least a region of anagricultural product, the method utilizing hyperspectral imaging andcomprising: (a) scanning a region along a sample of agricultural productusing at least one light source of a single or different wavelengths;(b) generating one or more hyperspectral images from the region; (c)determining a spectral fingerprint for the sample of agriculturalproduct from the one or more hyperspectral images; and (d) determiningat least one property including but not limited to a physical, chemicalor biological property relating to the scanned region; and (e) assigninga classification code to said region of agricultural product based uponsaid property.
 2. The method of claim 1, further comprising: scanningmultiple regions along the sample of agricultural product using the atleast one light source; and generating hyperspectral images from themultiple regions.
 3. The method of claim 1, wherein the propertyincludes a physical shape, form or size dimensions.
 4. The method ofclaim 1, wherein the property includes color.
 5. The method of claim 1,wherein the property includes moisture content.
 6. The method of claim1, wherein the property includes chemical compositional make-up.
 7. Themethod of claim 1, wherein the property includes identification offoreign matter.
 8. The method of claim 1, wherein the property includesan activity or a reactivity of the sample to one or more physicalstimulus.
 9. The method of claim 8, wherein said physical stimulusincludes exposure to electromagnetic radiation.
 10. The method of claim1, wherein the property includes an activity or a reactivity of thesample to one or more chemical stimulus.
 11. The method of claim 10,wherein said chemical stimulus includes exposure to aqueous liquids. 12.The method of claim 10, wherein said chemical stimulus includes exposureto non-aqueous liquids.
 13. The method of claim 1, wherein the propertyincludes an activity or a reactivity of the sample to one or morebiological stimulus.
 14. The method of claim 13, wherein said biologicalstimulus includes exposure to biological organisms.
 15. The method ofclaim 1, wherein the agricultural product includes tobacco.
 16. Themethod of claim 1, wherein the agricultural product includes tea. 17.The method of claim 1, wherein the agricultural product includes fruit.18. The method of claim 17, wherein the fruit includes grapes.